Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- prompt-tuning
+- prompt-learning
+- CLIP
+---
+
+# Dataset Introduction
+
+The standard datasets (except ImageNet) used for CLIP-based Prompt Tuning research (e.g., [CoOp](https://github.com/KaiyangZhou/CoOp)).
+
+Based on the original datasets, this repository adds **foreground segmentation masks** (generated by [SEEM](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)) of all raw images.
+
+Datasets contain: [ImageNet-1K](https://image-net.org/challenges/LSVRC/2012/index.php), [Caltech101](https://data.caltech.edu/records/mzrjq-6wc02), [Oxford Pets](https://www.robots.ox.ac.uk/~vgg/data/pets/), [StanfordCars](https://ai.stanford.edu/~jkrause/cars/car_dataset.html), [Flowers102](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/), [Food101](https://vision.ee.ethz.ch/datasets_extra/food-101/), [FGVC Aircraft](https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/), [SUN397](http://vision.princeton.edu/projects/2010/SUN/), [DTD](https://www.robots.ox.ac.uk/~vgg/data/dtd/), [EuroSAT](https://github.com/phelber/EuroSAT) and [UCF101](https://www.crcv.ucf.edu/data/UCF101.php).
+
+# Scope of Application
+
+Datasets are uitable for training and improving **foreground-supervised prompt tuning** methods. For example:
+
+- _Decouple before Align: Visual Disentanglement Enhances Prompt Tuning_
+
+- _FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models_
+
+Also, they are **fully compatible** with other original prompt tuning approaches.
+
+# Acknowledgements
+
+Our repository is built based on [DAPT](https://github.com/SII-Ferenas/DAPT) and [zhengli97](https://huggingface.co/zhengli97/prompt_learning_dataset).